Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications

Genetic Neural Network Prediction of Car Ownership Based on Principal Component Analysis

Authors
Qing Wang, Yewang Zhou
Corresponding Author
Qing Wang
Available Online January 2016.
DOI
https://doi.org/10.2991/icaita-16.2016.77How to use a DOI?
Keywords
principal component analysis; car ownership; genetic algorithm; neural network
Abstract
The prediction of the car ownership is the basic work for city traffic sustainable development. The paper carry on principal component analysis to influencing factors in the process of prediction of Wuhan City car ownership, determine the main components. Combining genetic algorithm with neural network, using genetic algorithm to optimize the weights of neural network, determine the initial weight values of neural network. Not only to improve the neural network training speed and generalization ability, but also overcome that the network is easy to fall into local minimum to a certain extent, then train the neural network, and carry on the prediction of car ownership. At last use a specific example to verify the prediction effect.
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This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
Series
Advances in Intelligent Systems Research
Publication Date
January 2016
ISBN
978-94-6252-162-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/icaita-16.2016.77How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Qing Wang
AU  - Yewang Zhou
PY  - 2016/01
DA  - 2016/01
TI  - Genetic Neural Network Prediction of Car Ownership Based on Principal Component Analysis
BT  - Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
PB  - Atlantis Press
SP  - 313
EP  - 315
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaita-16.2016.77
DO  - https://doi.org/10.2991/icaita-16.2016.77
ID  - Wang2016/01
ER  -